Patent Application
20220020049
The development of the internet has led to many goods and service providers offering their products and services online for sale. As a result, the field of online payments has developed rapidly wherein an internet-based user account is associated with a monetary source such as a bank account or a private account that is replenished with funds. Many merchants now extend various offers for online purchase transactions where goods and services can be researched and purchased. The online transactions are not only convenient to the user but are also cost-effective for the merchant as retail outlets need not be maintained. Users may pay for their online purchases using various payment modes or payment mechanisms such as credit cards, debit cards, online bank transactions, mobile payment platforms, etc. Accordingly, when purchasing a product, the user may not be aware of the most optimal payment modes that, if used, for the purchase transaction would provide the maximum benefit not only on monetary terms but also in terms of the user's preferences.
Features of the present disclosure are illustrated by way of examples shown in the following figures. In the following figures, like numerals indicate like elements, in which:
For simplicity and illustrative purposes, the present disclosure is described by referring to examples thereof. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be readily apparent however that the present disclosure may be practiced without limitation to these specific details. In other instances, some methods and structures have not been described in detail so as not to unnecessarily obscure the present disclosure. Throughout the present disclosure, the terms “a” and “an” are intended to denote at least one of a particular element. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on.
An Artificial Intelligence (AI) based payments processing system that analyzes invoice data including products and/or services for purchase and enables payments for the purchase via payment modes that are selected to optimize for savings and user preferences is disclosed. An invoice which is generated during a purchase transaction including the details regarding the products to be purchased such as the product identifiers, the cost of each of the products, the merchant selling the products, etc., is transmitted to the payments processing system prior to the completion of the purchase transaction. The payments processing system extracts the metadata from the invoice including details of the user's purchase. The invoice metadata can include the date/time of generation of the invoice, the locale of invoice generation, the merchant generating the invoice, etc. In response to receiving the invoice and extraction of the purchase details from the invoice, the payments processing system retrieves from a user profile of the user conducting the purchase transaction. Other details such as the identification details of the user, the user data also includes one or more payment modes that are used to pay for the user's purchases are also accessed. The payment modes include payment mechanisms that may be used to pay for the products and services. These payment mechanisms may include but are not limited to, credit cards, debit cards, cash cards, store gift cards, online bank accounts, mobile payment accounts, bitcoin accounts, etc. Additionally, the payments processing system accesses offer data including offers of considerations for the one or more products corresponding to the one or more payment modes wherein the considerations are received by the user in exchange for paying for the product with the corresponding payment mode as specified in the offer. The offer data is accessed from an offers entities dictionary that is built up by the payments processing system from various data sources as specified herein.
The payments processing system employs the data extracted from the invoice, the offers data, and data regarding the user's payment modes as provided by the user profile to identify at least two best offers—a savings based best offer and user behavior based best offer. Each of the best offers identifies and provides the user with a combination of payment modes that can be used to conduct the user's purchase transaction. The savings based best offer which includes the offer details such as the payment modes to be used to access the offer(s) for one or more products in the invoice, the consideration(s) to be realized by the user in accordance with the offer(s) and the amounts to be charged to each of the payment modes for the one or more products. The considerations can include monetary considerations and non-monetary considerations. The user behavior based best offer also includes the offer details as mentioned above but the offers are selected based not only on the considerations realized by the user but also on the user preferences as collected explicitly from the user at the time of registration to the payments processing system and implicitly on recording the user behavior as the user employs the payment processing system for purchase transactions over a time period.
The savings based best offer is generated by a savings oriented model that analyzes, via a scoring methodology, the invoice data, the offers, and the user profile information using a mix and match model and a dynamic decision tree structure model. The mix and match model generates different types of pairings or combinations of the products in the invoice and the different valid payment modes from the user profile. In an example, the combinations may map a product to a payment mode so that the payment mode is used to pay the total amount for that product. In an example, the mix and match model generates multiple product-payment mode combinations for one or more of the products wherein multiple payment modes can be used to pay for a product so that the total amount for the product is split between the different payment modes. If no offer associated with a payment mode exists for a specific product, then a frequently used payment mode or a default payment mode set by the user may be employed to pay for that specific product.
The different types of combinations thus generated can be evaluated based on weights assigned to the combinations. The product-payment mode combination weights reflect the considerations realized by the user and user preferences so that the product offers associated with the payment modes with greater considerations and/or more preferred by the user are weighted higher. Therefore, it can be appreciated that a product-payment mode combination for the specific product with no offers or less-preferred can likewise carry less or zero weight reflecting that no savings accrue to the user for the purchase of that specific product. The product-payment mode combinations thus generated are provided as input to the dynamic decision tree structure model which generates a path or a tree structure by further combining the different product-payment mode combinations across the different products. A cumulative score is calculated for the different paths or tree structures identified by the dynamic decision tree structure model. The product-payment mode combinations from the path with the highest score are provided as the savings based best offer to the user. The product savings model, therefore, employs a scoring based methodology to identify the best offer.
The user behavior based best offer is generated by a user behavior model that identifies the product—payment mode combinations not only based on the offers but also based on the user preferences as recorded in historical data associated with a user profile. This historical data can store information related to the prior user selections of offers, payment modes, etc. In an example, the user behavior model may be based on AI techniques such as the k Nearest Neighbor (KNN) methodology. The KNN model is trained via supervised training to identify the payment modes to be used to pay for the products included in the user's purchase transaction. Based on the training, different payment modes can be identified for different products by the user behavior model to generate the user behavior based best offer.
The payments processing system can be further configured to determine if the savings based best offer is the same as the user behavior based best offer i.e., the same payment modes are used to pay for the same products in the same proportions. If the savings based best offer is the same as the user behavior based best offer and the user has enabled an autopay option or a frictionless payment option, then payment for the user's purchase transaction is automatically enabled on the payment modes as suggested in one of the best offers. If the savings based best offer is not the same as the user behavior based best offer, then each of the best offers is presented to the user for selection. The payment for the user's purchase transaction is enabled on the payment modes specified in the best offer selected by the user. The user's selection can be recorded for updating the user profile and training.
The payments processing system as disclosed herein provides a technical solution to the problem of generating combinations of two or more datasets using different criteria. The technical solution involves not only usage of an AI-based methodology such as the kNN model to generate a weighted dataset of product-payment mode combinations but also provides for generation of enhanced combinations via a scoring methodology wherein the output of the mix and match model is fed to the dynamic decision tree structure model. Therefore, not only are the best product-payment mode combinations identified but also the best dataset of the product-payment mode combinations is identified in terms of the cumulative score calculated for each of the paths. One application of this technical improvement pertains to the field of online transactions wherein users are enabled to map their payment modes to the offers prevailing in the market based not only in terms of the offer considerations but also in terms of each user's preferences. Each payment mode brings with it a unique set of offers and promotions which the users may or may not be aware of. Furthermore, the users are also faced with the arduous task of figuring out the best possible option to make from an array of payment options. The payments processing system, therefore, provides for users, a hybrid payments platform with AI-enabled application that additionally employs scoring methodologies to allow intelligent routing of payment choosing the best payment method with maximum savings. For the merchants, the payments processing system enables, an intelligent platform with an open banking concept which allows the acceptance of payments from multi-payment modes for a single invoice, and time saver in the entire billing process.
The payments processing system 100 includes a data preprocessor 102, a smart wallet router 104, and a results processor 106. Besides, a models trainer 108 is also included to train the ML models within the payments processing system 100. The data preprocessor 102 includes an offers data processor 122 and a user data processor 124. The offers data processor 122 accesses information regarding the various offers associated with different products/product categories from different manufacturers, vendors in association with entities associated with the payment modes. Each offer in the offer information can be characterized by an offer id 182 or a settlement id and the offer attributes 184 such as but not limited to the conditions for the offer (e.g., the payment mode(s) to be used, any minimum purchases needed to access the offer, any specific merchant outlets to which the offer is confined, the time limits when the offer is valid, etc. The offer information can be received via different modalities. For example, the offer information can be received by processes such as but not limited to, collecting messages, monitoring online media and emails, using web scraping techniques on the web pages, downloading from the user device 180, etc. The offer information can also be received from offline resources such as images of advertisements from print media, coupons, vouchers, third party sources, etc. Thus, the offer information can be received in different formats such as image formats, text formats, etc. The data preprocessor 102, therefore, includes a format converter 126 that converts data received in any of the image, voice, etc. formats into a standard textual format such as a plain text format. Furthermore, the offers can relate to specific payment modes and the different considerations that can be realized by the user 150 in return for using the specific payment modes. The considerations can take various forms such as price discounts on the same product or other products for the same transaction or future transactions, cashback, loyalty points, airline miles, coupons, free products or services, etc. The offers data processor 122 can use Natural Language Processing (NLP) techniques in combination with Named Entity Recognition (NER) to recognize specific offers and the entities making the offers. The text extracted from the offer information by the format converter 126 can be parsed tokenized and tagged with parts of speech (POS) data in addition to the identification of entities with NER techniques based for example, on Spacy algorithms. The offers and the entities thus extracted from the offer information can be stored in an offers entities dictionary 128. In an example, the offers data processor 122 may further include a mechanism to automatically update the offers entities dictionary 128 to delete offers that may have expired based for example, on the temporal offer attributes.
The user data processor 124 receives and processes information for building the user profile 130. When the user 150 initially signs up with the payments processing system 100 for payment mode processing, the user 150 provides information such as the user demographics 132, personal identification information 134, and the plurality of payment modes 136 that are employed to pay for the various goods and services that the user 150 may purchase. As the user 150 begins to use the payments processing system 100 for processing payments for purchases, the user's behavior and habits can be recorded by the payments processing system 100 and employed to select and recommend one or more of the offers and payment modes. Accordingly, the data preprocessor 102 receives the invoice 118 to extract the details of the user's purchases. Again, techniques such as but not limited to, NLP, NER, etc. can be employed to extract the invoice data. More specifically, invoice data 172 such as the products purchased, the quantities of the products purchased, the price of each product, the total amount, date/time/location of purchase, the vendor, etc. is extracted from the invoice 118. The invoice data 172 thus extracted can be saved to the user profile 130.
The invoice data 172 is accessed by a smart wallet router 104 which selects one or more of the plurality of payment modes 136 to pay for the user's purchases based on the offers and the user behavior. If no offers are associated with the at least one payment mode of the plurality of payment modes 136, then a default payment mode can be set up in the user profile 130 to be automatically selected for payment. The smart wallet router 104 includes two ML models, a savings oriented model 142 and a user behavior model 144, to analyze the offer information and the data in the user profile 130 with respect to the invoice data 172 to select a payment mode to pay for the user's purchases. The savings oriented model 142 analyzes the invoice data 172 and the offer information from the offer entities dictionary 128 to identify one or more of the plurality of payment modes 136 that provide maximum monetary value to the user 150 associated with the user profile 130. The monetary value can be extracted directly for considerations such as cashback, discounts, etc. However, other considerations such as miles, loyalty points, etc., may have a specific monetary value associated with each unit. For example, a mile or one loyalty point can be associated with a specific monetary value to estimate their monetary value to the user 150. The payments processing system 100 may be pre-configured to assign weightage based on different criteria to the plurality of payment modes 136. In an example, each weight can correspond to an absolute score based on different types of considerations. However, such considerations may be updated based on consumer transactions. As part of consumer demographics, the weightage of each saving type can be dynamic, wherein
weightage=Avg.(System Score+/−1) Eq. (1)
Initially, the weightage may be initialized to 1, and based on the frequency of usage of the payment mode, the weightage may be updated, provided that saving type was applicable in that transaction. For example, savings types such as post-purchase cashback on cards/wallets may have the maximum weightage while savings type such as loyalty or rewards points may have lower weightage. The savings oriented model 142 therefore may select one or more of the plurality of payment modes 136 to pay for the user's purchases based on the maximum monetary value. In an example, a corresponding payment mode of the plurality of payment modes 136 can be selected by the savings oriented model 142 for each product so that the price for that product is paid entirely by the selected payment mode. In an example, a given product can be paid for by multiple payment modes of the plurality of payment modes 136. A savings based best offer 174 including the offer details, the payment modes to be used and the amounts to be charged to each of the payment modes in response to the invoice 118 is thus generated by the savings oriented model 142.
The user behavior model 144 also analyzes the invoice data 172, the offer information (which was accessed by the savings oriented model 142) given the user behavior data, i.e., the historical data 138 to select the payment modes that may be preferred by the user 150 based on historical data included in the user profile 130. In addition to the information provided by the user 150, the payments processing system 100 also records the user's selections of offers, payment modes, purchase habits, etc. as the historical data 138 within the user profile 130. The historical data 138 provides different weighting factors based on preferences gleaned from user behavior. For example, each of the plurality of payment modes 136 can be associated with a corresponding weight which can be set proportional to the frequency with which the user employs the particular payment mode for payments. Also, the user's 150 product preferences can be used to weight the offers so that offers for the user's preferred products on the user's preferred or most frequently used payment mode carry greater weight as opposed to offers to less preferred products on less frequently used payment modes. A user behavior based best offer 176 including the offer details, the payment modes to be used and the amounts to be charged to each of the payment modes in response to the invoice 118 is thus generated by the user behavior model 144.
The smart wallet router 104 may, therefore, output two results—each result corresponding to a scoring model and machine learning (ML) models. A results processor 106 accesses and compares the savings based best offer 174 and the user behavior based best offer 176. If both the results are the same, then the results processor 106 can determine from the user profile 130, if the user has opted for frictionless payment. If frictionless payment option is activated by the user in the user profile 130, then the payment modes from one of the savings based best offer 174 and the user behavior based best offer 176 are provided as the user-selected payment modes 178 to a merchant checkout mechanism 190 to pay automatically or trigger payments for the user purchases. If the user 150 is making purchases at a brick and mortar outlet, the merchant checkout mechanism 190 can include a checkout counter. If the user 150 is making purchases at an online outlet via the internet, the merchant checkout mechanism 190 can include an online cart that enables collecting and paying for the user's purchase. If the savings based best offer 174 differs from the user behavior based best offer 176, then both the results are provided to the results processor 106 for presentation to the user 150. The user's purchases are paid for using the payment modes in one of the savings based best offer 174 and the user behavior based best offer 176 based on the user-selected payment modes 178. The user selected payment modes 178 are provided to the merchant checkout mechanism to pay for the user's purchase. In an example, the user's selection can be fed back to update the user profile 130.
A models trainer 108 employs training data 114 to train the savings oriented model 142 and the user behavior model 144 to generate the results using supervised learning. The training data 114 can include details of various users such as the users' personal identification information, the various transaction metadata of the users such as the transactions dates/time/locations, the merchant associated with the transactions, the invoiced amounts, the settlement amounts (i.e., the considerations), types of considerations associated with each offer, the offers selected by the users, etc. In an example, the models trainer 108 can include a dimensionality reducer 116 which employs techniques such as principal component analysis to execute a dimensionality reduction process that removes from the training data 114, unwanted features which do not critically impact features for prediction. The principal component analysis works by way of establishing a correlation/covariance matrix between features to pare down the initial raw data to generate the training data 114 to be used for training the user behavior model 144.
The savings oriented model 142 uses attributes such as but not limited to the user demographics, the merchant demographics, the invoice data 172 to generate the savings based best offer 174. The savings oriented model 142 includes a mix and match model 202 and a dynamic decision tree scoring model 204. The results from the mix and match model 202 are further processed by the dynamic decision tree scoring model 204 to generate the savings based best offer 174. The mix and match model 202 uses data wrangling techniques to organize the information from the offers entities dictionary 128 so that an ascending or descending order of usage is determined for the plurality of payment modes 136 to pay for a given product in the invoice 118. The order for the plurality of payment modes 136 can be determined based on the monetary value of the considerations offered in response to using the corresponding payment mode to pay for the product. For each product in the invoice 118, a weight can thus be assigned to a product-payment mode combination. If the invoice 118 includes more than one product, similar orders of usage are determined for the plurality of payment modes to pay for each of the products by the mix and match model 202.
The output from the mix and match model 202 is accessed by the dynamic decision tree scoring model 204 which determines various paths that include different product-payment mode combinations wherein the payment modes are to be used to pay for the products in entirety in one example. In another example, the paths can include different product-payment mode combinations wherein multiple payment modes can be used to pay for one product. The product-payment mode combinations can be weighted based on different factors such as but not limited to, past user behavior e.g., the user's frequency of usage of the products/payment modes, the accessibility of the payment modes, the existence of offers associated with the payment modes, the amount of consideration associated with the offers, etc. The paths may be selected and cumulative scores are calculated based on the weights of the product-payment mode combinations that make up the paths. The savings based best offer 174, therefore, includes combinations of products-payment modes that are further combined into optimal paths to provide the user 150 with maximum savings.
The user behavior model 144 generates the user behavior based best offer 176 to include a combination of payment modes to be used to pay partially or completely for a given product while accounting for user preferences. The user behavior model 144 is based on k nearest neighbor (KNN) methodology. The user behavior model 144 is trained by the models trainer 108 to identify, for each product included in the invoice 118, a corresponding payment mode to be used to pay partially or entirely for the product. In an example, the user behavior model 144 is trained via supervised machine learning techniques. The data from the user profile 130 is accessed, and other users with profiles similar to the user profile 130 are identified via similarity techniques such as cosine similarity, etc. The similarity is determined based on three factors which can include the users, the payment modes, and the products.
In an example, wherein the user 150 signs up as a new user to use the payments processing system 100, sufficient historical data may not be initially available for the user behavior model 144. In such instances, only the savings based best offer 174 is employed to enable payment for the invoice 118. As the user 150 continues to use the payments processing system 100, the user's preferences are recorded in the historical data 138. The historical data 138 thus built up can be employed by the user behavior model 144 to identify similar users via the kNN methodology. Based on the selections of one or more of the payment modes and the offers made by the similar users, payment modes/offers may be suggested to the user 150 in the user behavior based best offer 176.
If it is determined that the savings based best offer 174 is not same as or identical to the user behavior based best offer 176, or if it is determined that the user 150 has not signed up for frictionless payments, then the savings based best offer 174 and/or the user behavior based best offer 176 are presented to the user 150 on the user device 180 by the payment details provider 302. When the user 150 selects one of the savings based best offer 174 or the user behavior based best offer 176, the selected offer is received by the user feedback receiver 306. The user feedback receiver 306 provides the user-selected offer to the payments processor 304 and the profile modifier 308. The payments processor 304 initiates payment for the user's purchase(s) on the payment modes and in a proportion specified in the user-selected offer. The profile modifier 308 modifies the user profile 130 by recording the user's selected payment modes in the historical data 138. In addition, the user selections may also be added to the training data 114 that is used to train the ML models. Therefore, the user's current selections drive future offers and payment mode options that are provided to the user 150.
At 412 it is determined if the user profile 130 includes historical data 138 regarding the prior purchases and payment mode selections of the user 150. If it is determined at 412 that there is no accumulated historical data regarding the user 150, the method proceeds to 420 to determine if the user has signed up for frictionless payments. If it is determined at 420 that the user 150 has signed up for frictionless payments, then the payment for the user's purchase is automatically initiated at 416 on the payment modes selected by the savings based best offer 174.
If it is determined at 412 that the user profile 130 includes the historical data 138, the method proceeds to 414 to employ the user behavior model 144 to generate the user behavior based best offer 176 wherein one or more of the plurality of payment modes 136 are selected to pay for the user's purchases, based not only on the considerations associated with the offers corresponding to one or more of the plurality of payment modes 136, but also based on prior user preferences as included in the historical data 138. As mentioned above, the user behavior model 144 is trained to employ data from the user profile 130 in order to identify other users of the payments processing system 100 with similar profiles by employing methodologies such as kNN methodology. The user behavior model 144, therefore, predicts that payment modes that would be preferred by the user 150 to pay for each of the products in the invoice 118 based on payment mode and offer preferences of the other users with similar profiles. Taking into account the user preferences enables the payments processing system 100 to allow for the possibility of certain user preferences superseding the monetary value of the offers. For example, an offer for a free food item may be rated highest by the savings oriented model 142 based on the monetary value. However, when the user preferences indicate that the user 150 does not consume such food product, then the offer for the food item is given lower value by the user behavior model 144. The payments processing system 100, therefore generates two offers—the savings based best offer 174 and the user behavior based best offer corresponding to the two different models.
Accordingly, at 416, it is further determined if the savings based best offer 174 and the user behavior based best offer 176 are identical in that each of the offers includes the same payment mode to be used to pay for each product for the same amount. If it is determined at 416 that the savings based best offer 174 and the user behavior based best offer 176 are identical, then it is further determined at 418 if the user has signed up for frictionless payments. If it is determined at 418 that the user 150 has signed up for frictionless payments, then payment for the user's purchase is automatically initiated at 420 on the payment modes selected by the savings based best offer 174.
If at 416 it is determined that the savings based best offer 174 and the user behavior based best offer 176 are not identical or if it is determined at 420 that the user has not signed up for frictionless payments, then one or more of the savings based best offer 174 and the user behavior based best offer 176 are displayed to the user 150 at 422 for selection. The best offer selected by the user 150 is received at 424 and the payment for the user purchase is initiated at 426 based on the user-selected payment modes 178 included in the user-selected best offer. In an example, the payments processing system 100 may also display the user-selected payment modes 178 on the merchant checkout mechanism 190 to update the merchant regarding the payment modes for the purchase.
It may be noted that in generating the user behavior based best offer 176 or the savings based best offer 174, if multiple offers become eligible for selection as the corresponding best offers, then multiple offers may be presented to the user as the best offers.
If it is determined at 758 that there is no carry forward amount to be charged to a next payment mode, it is further determined at 760 if there are more products to be paid for. If it is determined at 760 that there are further products to be paid for, the method returns to 752 to select the product. If it is determined at 760 that there are no more products to be paid for, the method terminates on the end block.
If it is determined at 758 that there is a carry forward amount, the carry forward amount is calculated at 762 as described herein based on the minimum offer condition. The next payment mode to be used to pay the carry forward amount is selected at 764 and the weight for the corresponding payment mode is increased by a predetermined number at 766. This is because whenever an amount to be charged to a payment mode is split between multiple payment modes, it implies that such as split transaction realizes greater savings/gains to the user 150. It may be noted that the selection of payment mode at 764 may be rather different from the selection of the payment mode at 754 because the selection of payment mode at 764 occurs from a subset of the remaining payment modes from the plurality of payment modes 136 after the payment mode previously selected at 754 is excluded. The method returns to block 756 wherein the amount to be charged to the selected payment mode is determined. Again, it is further determined at 758 if there is a carry forward amount for the product that needs to be charged to yet another payment mode. The method may thus proceed until it is determined at 758 there is no further carry forward amount and the next product is selected at 760 for further payment. If no more products remain to be paid for, the savings based best offer that corresponds to the split amounts is output for further processing at 768 as described herein and the method terminates on the end block.
Carry forwarded amount is calculated based on minimum offer condition and maximum absolute discount available. For example, if the offer specifies a 10% discount or a maximum 50$ off on a minimum purchase of $300 and if the product cost is 1000$ then 10% of 1000$ is 100$. As the maximum discount is only $50, the payments processing system 100, therefore, proposes to pay only $500 using this offer so that the user avails the $50 discount. Hence the mix ‘n’ match algorithm splits the amount in this scenario so that 50% is paid by the payment mode W1 and the remaining 50% is carry forwarded to the next payment mode to optimize the savings for the user 150. Whenever there is a possibility of a split amount, the WnSn weightage gets a positive upgrade because it impacts the overall saving positively. Carry forwarded amounts are generally used for saving types such as loyalty points or cashback on a particular payment mode.
The computer system 1300 includes processor(s) 1302, such as a central processing unit, ASIC or another type of processing circuit, input/output devices 1312, such as a display, mouse keyboard, etc., a network interface 1304, such as a Local Area Network (LAN), a wireless 802.11x LAN, a 3G, 4G or 5G mobile WAN or a WiMax WAN, and a processor-readable medium 1306. Each of these components may be operatively coupled to a bus 1308. The computer-readable medium 1306 may be any suitable medium that participates in providing instructions to the processor(s) 1302 for execution. For example, the processor-readable medium 1306 may be non-transitory or non-volatile medium, such as a magnetic disk or solid-state non-volatile memory or volatile medium such as RAM. The instructions or modules stored on the processor-readable medium 1306 may include machine-readable instructions 1364 executed by the processor(s) 1302 that cause the processor(s) 1302 to perform the methods and functions of the payments processing system 100.
The payments processing system 100 may be implemented as software stored on a non-transitory processor-readable medium and executed by the one or more processors 1302. For example, the processor-readable medium 1306 may store an operating system 1362, such as MAC OS, MS WINDOWS, UNIX, or LINUX, and code 1364 for the payments processing system 100. The operating system 1362 may be multi-user, multiprocessing, multitasking, multithreading, real-time and the like. For example, during runtime, the operating system 1362 is running and the code for the payments processing system 100 is executed by the processor(s) 1302.
The computer system 1300 may include a data storage 1310, which may include non-volatile data storage. The data storage 1310 stores any data used by the payments processing system 100. The data storage 1310 may be used to store the invoice 118, the data extracted from the invoice 118, the matching offers, the best offers that are generated and other data that is used by the payments processing system 100 during the course of operation.
The network interface 1304 connects the computer system 1300 to internal systems for example, via a LAN. Also, the network interface 1304 may connect the computer system 1300 to the Internet. For example, the computer system 1300 may connect to web browsers and other external applications and systems via the network interface 1304.
What has been described and illustrated herein is an example along with some of its variations. The terms, descriptions, and figures used herein are set forth by way of illustration only and are not meant as limitations. Many variations are possible within the spirit and scope of the subject matter, which is intended to be defined by the following claims and their equivalents.
